A unified partial likelihood approach for X‐chromosome association on time‐to‐event outcomes
Funding information: Grant sponsor: Canadian Institutes of Health Research (CIHR) Grant number: 145546.
Abstract
The expression of X‐chromosome undergoes three possible biological processes: X‐chromosome inactivation (XCI), escape of the X‐chromosome inactivation (XCI‐E), and skewed X‐chromosome inactivation (XCI‐S). Although these expressions are included in various predesigned genetic variation chip platforms, the X‐chromosome has generally been excluded from the majority of genome‐wide association studies analyses; this is most likely due to the lack of a standardized method in handling X‐chromosomal genotype data. To analyze the X‐linked genetic association for time‐to‐event outcomes with the actual process unknown, we propose a unified approach of maximizing the partial likelihood over all of the potential biological processes. The proposed method can be used to infer the true biological process and derive unbiased estimates of the genetic association parameters. A partial likelihood ratio test statistic that has been proved asymptotically chi‐square distributed can be used to assess the X‐chromosome genetic association. Furthermore, if the X‐chromosome expression pertains to the XCI‐S process, we can infer the correct skewed direction and magnitude of inactivation, which can elucidate significant findings regarding the genetic mechanism. A population‐level model and a more general subject‐level model have been developed to model the XCI‐S process. Finite sample performance of this novel method is examined via extensive simulation studies. An application is illustrated with implementation of the method on a cancer genetic study with survival outcome.
Citing Literature
Number of times cited according to CrossRef: 5
- Meiling Hao, Xingqiu Zhao, Wei Xu, Competing risk modeling and testing for X-chromosome genetic association, Computational Statistics & Data Analysis, 10.1016/j.csda.2020.107007, (107007), (2020).
- Osvaldo Espin‐Garcia, Kenneth Croitoru, Wei Xu, A finite mixture model for X‐chromosome association with an emphasis on microbiome data analysis, Genetic Epidemiology, 10.1002/gepi.22190, 43, 4, (427-439), (2019).
- Dongxiao Han, Meiling Hao, Lianqiang Qu, Wei Xu, A novel model for the X-chromosome inactivation association on survival data, Statistical Methods in Medical Research, 10.1177/0962280219859037, (096228021985903), (2019).
- Miriam Givisay Domínguez-Cruz, María de Lourdes Muñoz, Armando Totomoch-Serra, María Guadalupe García-Escalante, Juan Burgueño, Nina Valadez-González, Doris Pinto-Escalantes, Álvaro Díaz-Badillo, Pilot genome-wide association study identifying novel risk loci for type 2 diabetes in a Maya population, Gene, 10.1016/j.gene.2018.08.041, 677, (324-331), (2018).
- Wei Xu, Meiling Hao, Partial likelihood ratio test for X‐chromosome association models, Genetic Epidemiology, 10.1002/gepi.22157, 42, 8, (846-848), (2018).




